Exploratory data analysis in the context of data mining and resampling.

Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not...

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Main Author:	Chong Ho Yu
Format:	article
Language:	EN ES
Published:	Universidad de San Buenaventura 2010
Subjects:	exploratory data analysis data mining resampling cross-validation data visualization clustering Psychology BF1-990
Online Access:	https://doaj.org/article/cc4ccef837fb4519b1e7714936020f6b
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spelling	oai:doaj.org-article:cc4ccef837fb4519b1e7714936020f6b2021-11-25T02:24:06ZExploratory data analysis in the context of data mining and resampling.10.21500/20112084.8192011-20842011-7922https://doaj.org/article/cc4ccef837fb4519b1e7714936020f6b2010-06-01T00:00:00Zhttps://revistas.usb.edu.co/index.php/IJPR/article/view/819https://doaj.org/toc/2011-2084https://doaj.org/toc/2011-7922Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.Chong Ho YuUniversidad de San Buenaventuraarticleexploratory data analysisdata miningresamplingcross-validationdata visualizationclusteringPsychologyBF1-990ENESInternational Journal of Psychological Research, Vol 3, Iss 1 (2010)
institution	DOAJ
collection	DOAJ
language	EN ES
topic	exploratory data analysis data mining resampling cross-validation data visualization clustering Psychology BF1-990
spellingShingle	exploratory data analysis data mining resampling cross-validation data visualization clustering Psychology BF1-990 Chong Ho Yu Exploratory data analysis in the context of data mining and resampling.
description	Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.
format	article
author	Chong Ho Yu
author_facet	Chong Ho Yu
author_sort	Chong Ho Yu
title	Exploratory data analysis in the context of data mining and resampling.
title_short	Exploratory data analysis in the context of data mining and resampling.
title_full	Exploratory data analysis in the context of data mining and resampling.
title_fullStr	Exploratory data analysis in the context of data mining and resampling.
title_full_unstemmed	Exploratory data analysis in the context of data mining and resampling.
title_sort	exploratory data analysis in the context of data mining and resampling.
publisher	Universidad de San Buenaventura
publishDate	2010
url	https://doaj.org/article/cc4ccef837fb4519b1e7714936020f6b
work_keys_str_mv	AT chonghoyu exploratorydataanalysisinthecontextofdataminingandresampling
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Exploratory data analysis in the context of data mining and resampling.

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